Deep brain stimulation system and method with multi-modal, multi-symptom neuromodulation

ABSTRACT

Described here is a deep brain stimulation (“DBS”) approach that targets several relevant nodes within brain circuitry, while monitoring multiple symptoms for efficacy. This approach to multi-symptom monitoring and stimulation therapy may be used as an extra stimulation setting in extant DBS devices, particularly those equipped for both stimulation and sensing. The therapeutic efficacy of DBS devices is extended by optimizing them for multiple symptoms (such as sleep disturbance in addition to movement disorders), thus increasing quality of life for patients.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application claims the benefit of U.S. Provisional PatentApplication Ser. No. 62/411,266 filed on Oct. 21, 2016, and entitled“Deep Brain Stimulation System and Method with Multi-Modal,Multi-Symptom Neuromodulation,” which is incorporated herein byreference in its entirety.

FEDERAL FUNDING STATEMENT

This invention was made with government support under grant nos.N5098573, NS037019, and NS081118 awarded by the National Institutes ofHealth and under grant no. DGE-1069104 awarded by the National ScienceFoundation. The government has certain rights in the invention.

BACKGROUND OF THE INVENTION

Deep brain stimulation (“DBS”) has proven to be an effective treatmentoption for the motor symptoms of individuals with Parkinson's disease(PD). DBS has been used, for example, to treat medically intractablemotor symptoms like tremor, slowness of movement, and rigidity in PDpatients. But almost every patient with PD also suffers from some formof burdensome sleep disorder in addition to their motor and non-motorsymptoms (e.g., autonomic, cognitive, mood, pain, etc.), and in manycases such sleep disorders and other symptoms of PD are not beingeffectively treated. DBS therapy is used to treat the motor symptoms ofPD that manifest during the patient's awake-state, but no stimulationhas been designed to treat sleep disturbances, which are commonnon-motor symptoms.

PD patients particularly suffer from the lack of complementary DBStherapy tailored towards their sleep-related comorbidities. Hithertooverlooked in therapeutic DBS design, sleep disturbances have asubstantial impact on quality of life for this population. Sleepfragmentation, reduced total sleep time and efficiency, and rapid eyemovement sleep behavior disorder (RBD), in conjunction with excessivedaytime sleepiness, are reported in 74% to 98% of patients with PD. Itis likely that all PD patients experience some sleep disturbances,albeit to varying degrees of severity, and this distressing comorbidityof PD has not yet been widely treated using DBS. These symptoms can beeven more disabling and resistant to treatment than the motor symptomsof PD in some individuals. The number of people with PD is estimated at1 million in the United States, 1.2 million in Europe, and 10 millionworldwide, and these numbers are expected to double by mid-21st century.Due to the impact on quality of life and scarcity of effectivetherapeutic interventions, the National Institutes of Health-NINDS hasidentified the non-motor symptoms of PD, such as sleep, as an area ofhighest priority.

The current standard of care for patients with sleep disorders revolvesaround pharmaceutical interventions that vary in efficacy andreliability across patients and often come with undesirableside-effects. Drug treatments may include antidepressants,benzodiazepine sedatives, non-benzodiazepine hypnotics, antihistaminesfor insomnia, and stimulants for excessive daytime sleepiness. Thesemedications are helpful for some patients in improving their sleepdisturbances but are unfortunately also associated with significantadverse side effects and risks, such as cognitive impairment,dependence, and abuse. They also have high inter-subject variability intheir effectiveness, resulting in inconsistent results among differentpatients suffering from different sleep disturbances. For example, thecommon medication for REM sleep behavior disorder (RBD) is clonazepam (abenzodiazepine), which can have persistent daytime effects that resultin sedation, cognitive impairment, and falls. Moreover, sleep medicationefficacy is complicated by medication regimens that are administrated totreat other symptoms. For example, dopaminergic medications given totreat motor symptoms of patients with PD can themselves cause sleepissues like insomnia and daytime sleep attacks.

SUMMARY OF THE INVENTION

What are needed are more effective and less adverse treatments for sleepdisturbances and other disorders. A DBS-based solution would beapplicable in the subset of patients who already receive this therapy toreduce motor symptoms of PD, but it need not be limited to thispopulation. As an example, those that suffer severe treatment-resistantsleep disorders certainly stand to benefit from DBS tailored towardssleep improvement.

Sleep disorders are associated with a decline in quality of life inpeople of all ages, including children, adults, and seniors. Thedisclosed invention, in exemplary embodiments, involves DBS sensing andstimulation techniques that provide detection of sleep stages and relieffrom sleep disturbances, with the ability to continue providingeffective therapy for other symptoms being treated using a DBS system,such as motor symptoms in PD patients. Exemplary DBS systems arecontrolled so as to deliver diagnostics by tracking patient sleeparchitecture, as well as therapeutics by delivering optimal stimulationparameters at relevant sleep stages to improve overall sleep quality.Modulating sleep to treat sleep disorders via DBS would benefit not onlyPD patients, but others as well in the general population, such aspersons with intractable, treatment-resistant sleep disorders.Advantageously, a reduction in the need for medications and associatedside effects would be expected to increase a patient's quality of lifeand productivity, and could reduce healthcare costs.

In some exemplary embodiments, the invention may be implemented usingexisting DBS systems by enhancing/augmenting control mechanisms so as totarget sleep disorders (via, for example, firmware updates).Implementing augmented control mechanisms in existing DBS systems canprovide already-implanted patients with easy access to expandedtherapies without the risks associated with additional surgicalprocedures. For example, sleep disturbances in the population of PDpatients implanted with DBS systems may be more effectively diagnosedand treated by detecting and modulating sleep waves via the same deepbrain leads that are currently implanted to treat motor symptoms. Thismulti-objective approach to therapy (e.g., treating motor and sleepsymptoms with the same implant) is a paradigm shift that will bepreferred by patients, clinicians, and the health system because onlyone intervention is needed to address two or more symptoms.

This disclosure provides a novel approach to deep-brain stimulation(DBS) therapy for managing multiple symptoms simultaneously throughmulti-modal, targeted strategies that enable the treatment of variousneurological conditions and co-morbid conditions. Conditions treated byneuromodulation therapies—such as pain, movement disorders, andpsychiatric disorders—often have multiple symptoms. When a specificbrain target and therapy are used to treat one symptom, discomfort orother symptoms may arise. Additionally, many neurological disorderscarry with them comorbidities that may be ameliorated by DBS.

For example, PD has both motor symptoms such as rigidity and non-motorsymptoms such as sleep disturbances. Currently, the motor symptoms aretreated with DBS in the subthalamic nucleus (STN), globus pallidus (GP),and thalamus. The stages and quality of sleep can be detected byelectrophysiological recordings in the same brain regions that areadministered DBS to treat motor symptoms. In stimulating these brainregions, the waveforms and patterns/frequencies delivered to these sametargets can be used to promote/enhance sleep.

Thus, certain embodiments of this invention augment DBS therapy to treatboth the motor and non-motor symptoms of PD by incorporating novelalgorithms to detect, monitor, and improve sleep quality. Thesealgorithms will incorporate circuit specific therapy signals at theappropriate times, as will be further discussed below (see, e.g., FIG.3), to promote sleep. One can detect sleep from deep brain structures,while stimulating therein (see, e.g., FIG. 4). This multi modal andmulti-symptom therapy can employ, in other embodiments, novelelectrodes/leads to span multiple brain targets and multiple sensors orwearables to best inform the DBS system of the patient's clinical state.Multiple and simultaneous waveforms can be used to target specific braincircuits which cause specific symptoms. Various stimulation modalitiesmay be used to accomplish therapeutic neuromodulation (i.e. electrical,magnetic, optical, sound/ultrasound, etc.) or targeted drug deliverytherapies. For example, the sensors may trigger an electrical modulationresponse or communicate and trigger the flow of drug via an implanteddrug pump, or trigger an alert on the patient's device programmer totake their scheduled or PRN (i.e., on an “as needed” basis) oralmedications. Similarly, the deep brain leads may modulate viaoptogenetic stimulation in conjunction with triggering externalultrasound or magnetics stimulators for a combined effect.

The invention thus provides, in various embodiments, a novel method ofspanning multiple brain targets for multiple symptoms, which may beemployed at different times depending on the clinical symptoms andclinically relevant states, such as sleep versus awake states. Inparticular, the use of novel DBS waveforms and stimulation parameters isuseful for inducing a sleep state in subjects with sleep disturbances asa comorbidity, as well as subjects with idiopathic sleep disorders.

A multi-modal stimulation coupled with multi-symptom feedback can offeran effective approach to treat both the primary symptoms of aDBS-ameliorable disease, along with any DBS-ameliorable comorbidities.Not only are such natural comorbidities treated, but DBS-inducedside-effects are controlled and counteracted by monitoring them in realtime. Different parameters of stimulation can be delivered to the sametargets stimulated during the day to promote quality sleep;electrophysiological biomarkers for the sleep state can be monitored toadjust stimulation accordingly.

DBS is often used to counteract the motor symptoms of a diagnoseddisease, but has not seen widespread use in treating multiple braintargets for multiple major symptoms. In the case of PD-related sleepdisorders, the use of pharmacological interventions may be minimized byutilizing the already-implanted DBS hardware and deliveringsleep-improving stimulation when needed.

In accordance with one aspect of the present disclosure, a method isprovided for treating a sleep disorder using deep brain stimulation(DBS). The method includes steps of: receiving and processing readingsfrom a neurosensor implanted in the brain of a subject to evaluate acurrent neural state of a subject, the neurosensor being connected to animplantable pulse generator (IPG); and based on the current neuralstate, stimulating the brain of the subject via a neurostimulator toinduce a modified sleep state, the neurostimulator being connected tothe IPG.

In accordance with another aspect of the present disclosure, a method isprovided for treating two or more symptoms of a subject using deep brainstimulation (DBS). The method includes steps of: receiving andprocessing readings from a neurosensor implanted in the brain of asubject to evaluate a current neural state of a subject, the neurosensorbeing connected to an implantable pulse generator (IPG); and based onthe current neural state, stimulating the subject's brain via aneurostimulator to induce a modified neural state, the neurostimulatorbeing part connected to the IPG.

In accordance with still another aspect of the present disclosure, asystem is provided for treating a sleep disorder using deep brainstimulation (DBS). The system includes: a lead including a set ofelectrodes for taking readings from, and stimulating, one or moreregions of a subject's brain; and an implantable pulse generator (IPG)including a neurostimulator and a neurosensor for using the set ofelectrodes to both stimulate neurons and obtain readings from thesubject's brain. The IPG in turn includes a controller configured toreceive and process electrical data from the subject's brain via theneurosensor to detect a current neural state of the subject; and basedon the current neural state, stimulate the subject's brain via theneurostimulator to induce a modified sleep state.

In accordance with yet another aspect of the present disclosure, a deepbrain stimulation (DBS) system is provided for treating a sleep disorderusing deep brain stimulation. The system includes a controller includinga processor and instructions that, when executed by the processor,configure the DBS system to: receive and process readings from aneurosensor to evaluate a current neural state of a subject, theneurosensor being implanted in a brain of a subject and connected to animplantable pulse generator (IPG); and based on the current neuralstate, stimulate the brain of the subject via a neurostimulator toinduce a modified sleep state, the neurostimulator being connected tothe IPG.

In accordance with yet another aspect of the present disclosure, amethod is provided for controlling a deep brain stimulator (DBS)implanted in a brain of a subject. The method includes the steps of:receiving electrical data obtained from a sub cortical structure of thebrain of the subject; determining a sleep stage of the subject based onanalyzing the electrical data; and adjusting control of the DBS based ondetermining the sleep stage of the subject.

In accordance with still another aspect of the present disclosure, amethod is provided for modulating sleep in a subject. The methodincludes the steps of: stimulating a sub cortical structure of a brainof the subject using a deep brain stimulator (DBS) implanted in thebrain of the subject to alter a sleep stage of the subject, stimulatingcomprising applying a patterned stimulation to the sub corticalstructure using the DBS.

In accordance with yet another aspect of the present disclosure, a deepbrain stimulation (DBS) system is provided. The DBS system includes: acontroller including a processor and instructions that, when executed bythe processor, configure the DBS system to: receive electrical dataobtained from a subcortical structure of a brain of a subject; determinethat the subject is in a REM sleep stage or an NREM sleep stage based onanalyzing the electrical data; and adjust therapeutic stimulation of theDBS based on determining that the subject is in the REM sleep stage orthe NREM sleep stage.

In accordance with still another aspect of the present disclosure, adeep brain stimulation (DBS) system is provided. The DBS systemincludes: a controller including a processor and instructions that, whenexecuted by the processor, configure the DBS system to: stimulate asubcortical structure of a brain of a subject to alter a sleep stage ofthe subject, stimulating comprising applying a patterned stimulation tothe subcortical structure.

The foregoing and other aspects and advantages of the invention willappear from the following description. In the description, reference ismade to the accompanying drawings which form a part hereof, and in whichthere is shown by way of illustration preferred embodiments of theinvention. Such embodiments do not necessarily represent the full scopeof the invention, however, and reference is made therefore to the claimsherein for interpreting the scope of the invention.

BRIEF DESCRIPTION OF THE DRAWINGS

FIGS. 1A and 1B show an exemplary deep brain stimulation (“DBS”) systemconnected to an implantable pulse generator (IPG), where the DBS systemincludes one or more leads implanted in the brain of a subject.

FIG. 2 is a close-up view of the leads of the DBS system of FIGS. 1A and1B, showing that each lead includes a set of electrodes, and depictingan alternative embodiment in which the IPG is integrated into theportion of the DBS that is implanted in the brain.

FIG. 3 is a functional schematic of an exemplary DBS system to be usedto treat sleep disturbances in, e.g., patients with Parkinson's disease(PD). The system is capable of sleep detection and DBS control tosimultaneously alleviate sleep disturbances and motor systems.

FIG. 4 is a time-frequency spectrogram of a unipolar subthalamic nucleus(STN) recording from a parkinsonian non-human primate (“NHP”) before,during, and after a period of low-frequency (5 Hz) deep brainstimulation. This represents simultaneous sensing and stimulationthrough an STN-DBS electrode, and data show sleep can be identifiedthrough spectral analysis and detection algorithms based on recordingsfrom another contact on the same DBS electrode array used forstimulation.

FIG. 5 provides a flowchart with an exemplary sequence of eventsaccording to various embodiments of the invention in which sleepdisturbances are to be treated.

FIG. 6, related to oscillatory activity, provides power spectraldensities (PSD) that reveal differences in oscillatory activity betweenawake and sleep states in two subjects. Recordings from the primarymotor cortex (M1), subthalamic nucleus (STN), and internal and externalsegments of the globus pallidus (GPe/i) are shown. It is noted that thedisease state causes a change in the structure of the PSD, but that inboth the disease and healthy states, sleep alters that structure aswell. In other words, whether normal or parkinsonian, sleep state can bedetected based on characteristics of the oscillatory activity (e.g.,amplitude of low frequency power and/or relative levels of high and lowfrequency power).

FIG. 7, related to cross frequency correlations, shows that phaseamplitude coupling (PAC) emerged in the parkinsonian state in the GP ofa NHP subject. This biomarker of disease state was present in the awakecondition but reduced during sleep. It is notable that during the earlysleep stages reflected here, the parkinsonian symptoms of rigidity werealso reduced. This PAC information may also be used to inform sleepstaging. The static images represent average activity recorded over manyseconds to minutes in each state (awake, sleep). (Analysis can also beperformed in more real time as shown in FIG. 8.)

FIG. 8 illustrates, via a spectrogram and a time-comodulogram, how thepower and PAC, measured from the GPe of a NHP subject in theparkinsonian condition, evolved over periods in the wake and sleepstates.

FIG. 9 shows phase amplitude coupling (PAC) is altered in PD, and thatPAC measures can provide information about the sleep state (M1recordings shown).

FIG. 10 shows sleep spindles associated with sleep simultaneouslyrecorded in both cortical regions (ECoG on primary motor cortex, “MCECoG LFP”) and in the STN (DBS lead, “STN DBS LFP”). Spindles aredetected using wavelet spectrograms that have the time resolution toenable their detection. The maximum power of the spindle in the STN canoccur at a lower frequency than for spindles in cortical regions.

FIG. 11 shows a schematic diagram of an embodiment of a sleep modulationsystem.

FIG. 12 shows a schematic of an embodiment of an artifact removal systemshown in FIG. 11.

FIG. 13 shows a schematic of a reference frequency generated byouter-loop scheduler.

FIG. 14 shows a spectrogram showing the modulation of power of STN localfield potentials (LFPs, differential potentials) depending upon thephase of phasic stimulation of the STN of a non-human primate.

DETAILED DESCRIPTION OF THE INVENTION

Described here is a deep brain stimulation (“DBS”) system and methodthat uses electric potentials or/and electromagnetic fields for thediagnosis, monitoring, and treatment of sleep-related disorders. Anexample of such a DBS system is represented in FIGS. 1A and 1B. The DBSsystem 10 includes an implantable pulse generator (IPG) 12 that iselectrically connected to one or more leads 14 implanted in the brain ofa subject. In various embodiments the subject may be a human, anon-human primate (NHP), or another animal. In certain embodiments (e.g.FIG. 1A), the IPG 12 contained in a separate housing that may beimplanted in the thoracic region of the subject and connected to theportion of the DBS system 10 (e.g. which includes the leads 14) that isimplanted in the brain, for example by a cable as shown in FIG. 1A. Inother embodiments, the IPG 12 may be integrated into the implantedportion of the DBS, for example as indicated in FIG. 2.

The IPG 12 includes a neurostimulator 16 for sending electrical pulsesinto the brain to generate electric currents that stimulate neurons (andthus influence neural activity at a target site), and a neurosensor 18for reading electrical signals from a target site in the brain. Atransceiver 20 (which may use, e.g., Bluetooth or other wirelesscommunications technology) can be included to allow data (such as sensedsignals) to be transmitted to another system, and/or to allow data (suchas commands and stimulation patters) to be received. A controller 22,which can be accessed using, e.g., a remote control via transceiver 20,includes a processor and memory for storing instructions (to be executedby the processor) for processing data (such as sensed brain activity),initiating stimulation, etc. Battery 24, which may be inductivelyrechargeable, is used as a source of energy for the IPG 12. Referring toFIG. 2, the one or more leads 14 of IPG 12 may include a first lead 30having a first set of electrodes 32, and a second lead 34 having asecond set of electrodes 36. Each electrode can be independently drivenusing stimulation signals generated by the neurostimulator 16 undercontrol of the controller 22, which may additionally include adigital-to-analog converter.

In exemplary embodiments, the DBS system enables the assessment of sleeparchitecture and quality (i.e., NREM and REM sleep) through theimplanted DBS device without the need for typical polysomnographyrecordings in a sleep clinic or hospital. This would allow sleepspecialists to assess the patient's sleep over time and inform treatmentchoices. Additionally, the DBS system modulates brain activity torestore a patient's normal sleep architecture by addressing issues withparticular sleep stages. This may be accomplished, for example, byinducing and/or extending slow wave sleep and/or altering stimulationduring REM sleep to disrupt the abnormal muscle activation that occursduring REM sleep behavior disorder (RBD).

Referring to FIG. 3, an exemplary DBS system includes at least one DBSlead with one or more electrodes used for sensing, and one or moreelectrodes used for stimulation. Signals sensed using the electricallead of the DBS system may be relatively faint, and can thus beamplified using an amplifier (which may be part of the neurosensor 16 inFIG. 1B) and analyzed for sleep detection. Analysis may occur separately(by, e.g., an external/separate computing system that receives readingsfrom the IPG via the transceiver 20, such as a mobile device or aworkstation), and/or by the IPG itself (using, e.g., controller 22).Identified sleep states can be provided by the external system to theDBS controller, which can process and initiate stimulation of a targetedregion of the brain. The IPG is preferably able to receive user input(such as instructions on when or how to initiate stimulation) wirelesslyusing its transceiver.

Preclinical experiments in normal and parkinsonian non-human primates(“NHP”) have shown that the DBS system allows for the detection of earlystages of sleep using DBS implants in subcortical structures whilestimulating at low frequencies. FIG. 4 shows data from simultaneoussensing and stimulation via DBS electrodes in awake and sleep states.The time-frequency spectrogram (400) of a unipolar STN recording in FIG.4 shows that a low-frequency stimulation (405) at 5 Hz was imposed forabout a minute starting at about time 30 seconds. Periods of drowsinessand sleep followed, which can be identified using spectral analysis anddetection based on recordings from another contact on the same DBSelectrode array used for stimulation.

As can be seen in FIG. 4, sleep lasting for approximately 10 seconds(410) was identified based on electrode readings beginning atapproximately time 40 seconds. This first sleep period corresponded withvideo recordings (i.e., eye camera videos) and field potentialrecordings from the M1 motor cortex (415), both of which providevalidation for the detected sleep. Similarly, early non-REM sleeplasting for approximately 20 seconds (420) was identified based onelectrode readings starting at approximately time 65 seconds. Thissecond sleep period was also validated using video recordings and fieldpotential recordings from the M1 motor cortex (425).

Readings that indicate sleep (and different stages thereof) can vary fordifferent individuals, but is expected to be correlated with lower powerlevels at certain frequencies. For example, in spectrogram 400, sleepwas observed with power levels ranging from −100 to −95 dB/Hz (which islower than surrounding power levels that mostly fell in the range of−115 to −100 dB/Hz) at lower frequencies of 2 to 8 Hz. Sleepmeasurements can be used for device tuning, assessment of the efficacyof the DBS sleep therapy, and automatic adjustment of DBS parameters indifferent vigilance states. Different patterns of DBS may modulateoscillatory activity associated with non-REM and REM sleep stages. Basedon observations in the laboratory with a non-human primate,low-frequency stimulation in the thalamus may promote drowsiness andsleep; this contrasts with standard DBS therapy, which delivershigh-frequency stimulation.

Referring to the exemplary flowchart in FIG. 5, the DBS system may firstbe activated by the patient or by the patient's healthcare provider(500). This may involve the patient using a remote control or externalcomputing device to turn the DBS system on, or to otherwise initiate itssleep control protocols. A desired sleep state may be entered and/orconfirmed by the patient/healthcare provider (505), if multiple sleepstates are available. For example, the patient may wish to indicate thats/he intends to sleep for the entire night, and would thus like acorresponding amount of REM sleep. Alternatively, the patient may submitthat a one-hour “nap” is desired, or that no sleep is desired, in whichcase the DBS system may be focused on monitoring brain activity (usingsensing electrodes) without stimulating for inducing a sleep state. Suchmonitoring may be particularly useful for, e.g., calibrating the DBSsystem before initial use, to fine-tune the system during continued use,or to incorporate refined parameters based on data not already availablefrom prior recordings from a particular patient.

The DBS system may then acquire data using sensing electrodes (510), andprocess that data to identify the patient's current neurological state(515). This may provide useful information in determining how best tomodulate signals to achieve the desired sleep state. Based on thecurrent state, and the patient's desired state, a set of parameters forstimulation can be selected (520) and stimulation initiated, ifappropriate (525). If sleep is not to be induced, and only monitoring isto be conducted, then there may be no sleep-inducing stimulationinitiated.

The DBS system may receive inputs from the patient (or healthcareprovider) (530), who may have just experienced stimulation intended topromote sleep. If the patient has changed his/her mind, or decided thatthe effects are undesirable, the patient may indicate that s/he wouldlike to stop the treatment (535). If so, the stimulation is ended (540),and it is determined (based on a prior entry by the patient/healthcareprovider, or in response to a new query) whether monitoring ofneurological activity should continue (545). If monitoring is tocontinue, the DBS system takes readings (550) for a set duration, untilcertain observations are made, or until instructed to stop by thepatient/healthcare provider (555). If monitoring (545) is not tocontinue, or if readings are to stop (555), then the DBS system may endits sleep-related protocols (570).

If the patient/healthcare provider does not experience a desirableoutcome (e.g., if the stimulation does not induce drowsiness but insteadawakens the patient or brings about undue anxiety, or if stimulation hascaused discomfort), the patient may provide “negative” feedback (560),by, e.g., identifying undesirable experiences. If there is no negativefeedback, or if feedback is positive, the DBS system again acquires datausing sensing electrodes (510) to identify the patient's current state(515), and either elects to continue with the same stimulationparameters as before (520), or modifies parameters based on the changesin the patient's current state (as determined from readings (510) orfeedback (560)). If the patient provides negative feedback (560), theDBS system may determine whether a suitable alternative stimulationstrategy is available (565), based on the patient's current state,desired outcome, past experiences, and/or data available fromstimulation of patients with comparable neurological indicators.

If no alternative is available, the DBS system may end (570) the sleeppromotion process. If a potential alternative stimulation strategy isknown, the DBS system acquires (510) and processes (515) recorded datato better understand the patient's current state and determine whichparticular alternative/parameters (if any) are suitable to try (520). Itis noted that if there is no intervention from the patient/healthcareprovider (530)—i.e., if there is no “stop” command (535), and if the DBSsystem has not exhausted all its available strategies to no avail(565)—then the DBS system continues in a loop in which data is recorded(510), processed (515), and (potentially new or revised) stimulationparameters are selected (520) and applied (525).

In exemplary versions, the therapeutic being developed may beimplemented as a software suite to be incorporated into DBS implanttechnology (e.g. Medtronic Activa RC+S) for PD. The softwarecharacterizes sleep architecture based on the physiological signalsrecorded from the implanted DBS leads and adjusts DBS parameters inorder to address patient-specific sleep disorders. The ability to detectspecific sleep stages will open the possibility of a targeted therapythat treats specific sleep disorders such as REM sleep behaviordisorder, in which patients have abnormal muscle activation during theREM sleep stage and for which simulation may be optimized to restoreatonia or minimize movement. For patients with insomnia (difficultyfalling asleep and/or frequent awakenings), DBS stimulation may beoptimized to promote and extend slow waves associated with non-REMsleep. This potential therapy was identified based on observations inNHP that sleep may be detected based on local field potential recordingsfrom DBS implants in the subthalamic nucleus (STN) and external and/orinternal segments of the globus pallidus (GPe/i). The high connectivityof basal ganglia nuclei and sleep circuits (e.g., via projections to andfrom the pedunculopontine nucleus and the thalamus) provides a basis foroptimizing DBS settings to treat sleep disturbances in PD patients whoreceive DBS. As suggested above, the ability to incorporate thesealgorithms into existing DBS technologies without the need for newsurgical interventions is a significant advantage.

During a routine deep-brain stimulation experiment in a NHP, it wasobserved that upon turning on low-frequency stimulation (traditional DBSis delivered at high frequencies, around 130 Hz), the NHP dozed into thefirst state of sleep. Upon turning off the low-frequency stimulation,the NHP came back into an alert state. This effect was repeated severaltimes. In other experiments with animals that were implanted in the STNand GPi with scaled-down versions of human DBS leads, data was collectedusing the DBS leads (not just simply stimulating through the leads)while animals were in a passive resting condition. These experimentswere focusing on awake rest recordings, and so it was not initiallydesired that animals fall asleep; nonetheless, animals occasionallyspontaneously fell asleep. Sleep epochs were especially common after theanimal became parkinsonian via administration of the neurotoxin MPTP.Marked changes in the neural activity patterns in these deep brainstructures during sleep were discovered. These findings, combined withthe findings that non-traditional stimulation frequencies may inducesleep, led to development of closed-loop algorithms for monitoring andmodulating sleep.

The sleep detection and staging algorithms may employ two recordingmodes (monopolar and bipolar) to detect sleep stages and signatures ofPD associated with their sleep disorder. Monopolar recordings (in whichthe reference electrode is distant, e.g. at the IPG housing, e.g. FIG.1A) from individual contacts on the DBS array are used to capturelong-range brain rhythms that are associated with particular sleepstages as they are traditionally defined (non-REM stage 1-4, REM, etc.).Bipolar potential recordings (e.g. reference electrode is a nearbycontact on the DBS array) are used to measure short-range neuronalactivity at high-frequency that is also associated with sleep (e.g.sleep spindles).

Information about sleep stages can be determined by evaluatinglow-frequency brain rhythms characteristic of sleep that exist in bothhealthy and parkinsonian subjects, as supported by evidence fromnon-human primate studies. Moreover, certain electrophysiologicalbiomarkers characteristic of the disease state (e.g. phase amplitudecoupling, PAC) that are prominent in the awake state but are altered insleep state (e.g., reduced strength and/or change in phase of coupling)may be used to characterize sleep stages. For example, a strong PACmeasured in the globus pallidus external segment in the awakeparkinsonian animal (not present in the healthy animal) diminishes whenthe animal becomes drowsy and falls asleep. Throughout a full sleepcycle of neural activity, it is expected that PAC changes observed inearly sleep stages would be different during REM sleep stage and maycomplement the spectral analysis techniques described below.

In order to promote sleep, sleep-specific stimulation through a DBSarray may be activated at appropriate times (or stages of sleep), assuggested above. Stimulation patterns can include traditional staticfrequency stimulation, as well as variable frequency approaches. Onesuch example would be a high-frequency to low-frequency “chirp” signal,which resembles the “slowing” of oscillations in the brain as a patientfalls into deeper sleep-states. A variable transition of f0 to f1 can beattempted, the rate at which the frequency varies can be modulated, andeffects on sleep observed. Burst stimulations can be tried, by varyingthe inter-burst frequency, intra-burst frequency, and the length of theburst epoch.

Preliminary data collected in NHPs has demonstrated similar sleeppatterns as humans, and in the parkinsonian state, NHPs exhibit sleepdisturbances and excessive daytime sleepiness similar to people with PD.The NHPs studied were adult female rhesus macaques (Macaca mulatta, aged13 and 17). All surgery was performed using aseptic techniques underisoflurane anesthesia. Pre-operative cranial CT and 7-T MRI images wereincorporated into the Monkey Cicerone neurosurgical navigation programto facilitate surgical planning of a titanium cephalic chamber targetingthe STN, GPe and GPi. Extracellular microelectrode mapping confirmed thelocation of target nuclei. Each animal was then implanted in both theSTN and GP with 8-contact scaled versions of human DBS leads (0.5 mmcontact height, 0.5 mm inter-contact spacing, 0.625 mm diameter, NuMED,Inc.) using known methods. Each animal was subsequently implanted in thearm area of primary motor cortex (M1) with a 96-channel Utahmicroelectrode array (Pt-Ir, 1.5 mm depth, 400 um inter-electrodespacing, Blackrock Microsystems) using known surgical methods. Pt/Irreference wires were placed between the dura and skull adjacent to thearray. During array implantation surgery, M1 was identified based onsulcal landmarks, and the arm area was localized based onintra-operative stimulation of the cortical surface using a stainlesssteel ball electrode (Grass Technologies). The motor cortex recordingswere used as a proxy for cortical EEG recordings and confirmation ofsleep state along with eye opening and closing. Locations of DBS leadswere verified in one NHP subject histologically using frozen coronalsections (50 um thick) that were imaged and visualized in Avizo 3Danalysis software (FEI). DBS lead locations were verified using fusedpre-implantation MRI and post-implantation CT images in the other NHPsubject.

Once data were collected in the normal state, animals were renderedparkinsonian by systemic (intramuscular) and intra-carotid injections ofthe neurotoxin 1-methyl-4-phenyl-1,2,3,6 tetrahydropyridine (MPTP). Onesubject received six intramuscular injections (0.3-0.4 mg/kg each, total0.19 mg/kg), while the other received three intramuscular and oneintra-carotid injections (0.3-0.4 mg/kg each, total 0.13 mg/kg). Forboth subjects, data were gathered after a stable parkinsonian state wasachieved, beginning approximately one month after the last MPTPinjection and continuing for one to two months. Motor symptoms wereassessed using a modified Unified Parkinson's Disease Rating Scale(mUPDRS) which rated axial motor symptoms (gait, posture, balance) aswell as upper and lower limb rigidity, bradykinesia, akinesia and tremoron a 0-3 scale (0=normal, 3=severe, maximum total score of 18). Onesubject received daily dopaminergic treatment (carbidopa/levodopa 25/100mg tablets) at the end of the day's experimental sessions to facilitateanimal's care in its home enclosure; however, all neurophysiologyrecordings were conducted a minimum of 16 hours after the last treatmentdose. Animals were in a moderate to severe parkinsonian state for axialsymptoms and bradykinesia, rigidity, and akinesia.

Neurophysiological data were collected using a TDT workstation (TuckerDavis Technologies) operating at ˜25 KHz sampling rate. Signals werebandpass filtered (0.5-700 Hz) and down-sampled to ˜3 kHz for analysis.Monopolar LFPs from the STN and GP were recorded referenced to a groundscrew on the animal's head. Bipolar LFPs were generated by subtractingrecordings from adjacent contacts of the DBS leads (e.g. LFP C0-C1represents the signal created by subtracting contact 1 from contact 0).A mean M1 LFP was obtained by averaging recordings from all 96 channelsin the array and used for confirmation of sleep state along with eyeopen state. All data were collected during a resting state while theanimal was seated in a primate chair with its head fixed. Time periodswith movement artifacts were identified by high amplitude broadbandpower in the time-frequency spectrogram (spectral analysis describedbelow) and excluded from further analysis.

Regarding sleep detection algorithm, sleep stages N1, N2, and N3 aretraditionally classified via identification of K-complexes, slow waves,and delta oscillations (0.4-4 Hz) in electroencephalography recordings.It was observed that a proxy of these low-frequency signal features isthe oscillatory activity recorded from monopolar potentials in basalganglia nuclei (STN, GPi, GPe). To detect non-REM sleep (N1, N2, or N3stages) particularly, the instantaneous power of the monopolar potentialwas used. A measure of the low-frequency power at each sample time kdenoted by P(k) is computed by bandpass filtering (any filteringtechnique) the monopolar potential in the 0.1-7 Hz range, obtaining theamplitude envelope of the low-frequency oscillations via the Hilberttransform or by rectifying the signal, and smoothing the envelope via alow-pass filter of cutoff 0.2 Hz (using any filtering technique). Athreshold Pt is used to classify, given the low-frequency powerenvelope, whether the subject is in the NREM-sleep or awake state. Thethreshold Pt is computed as follows. First, a prolonged period of timein which the subject is in the awake state with its eyes open wasselected by inspecting video recordings. Then, the maximum value of thelow-frequency power envelope during this period was selected as thethreshold Pt. The expression below summarizes how at each sample k thevigilance state Sv(k) was estimated based on the low-frequency power inthe basal ganglia nuclei.

Sv(k)=1(awake) if P(k)<Pt

0 (sleep) otherwise

The detection algorithm was validated by quantifying the instantaneouseye-opening of the subjects from video recordings and the low-frequencyoscillations from recordings in the motor cortex (Utah electrodearrays).

FIG. 7 illustrates the outcome of the sleep detection algorithm usingrecordings from the STN and how the algorithm compares with sleepassessment using video and cortical recordings. FIG. 7, which is relatedto cross frequency correlations, shows that phase amplitude coupling(PAC) emerged in the parkinsonian state in the GP of a NHP subject. Thisbiomarker of disease state was present in the awake condition butreduced during sleep. It is notable that during the early sleep stagesreflected here, the parkinsonian symptoms of rigidity were also reduced.This PAC information may also be used to inform sleep staging. Thestatic images represent average activity recorded over many seconds tominutes in each state (awake, sleep). Spectral analysis can also beperformed in more real time as shown in FIG. 8.

FIG. 8 illustrates, via a spectrogram and a time-comodulogram, how thepower and PAC, measured from the GPe of a NHP subject in theparkinsonian condition, evolved over periods in the wake and sleepstates. During periods in the wake state specifically, the spectrogramshows persistent elevated power of low- and high-frequency oscillationsassociated with the parkinsonian condition. During the sleep stateparticularly, the power of high-frequency activity (160 to 240 Hz),associated with the parkinsonian condition and PAC, decreased asevidenced by the spectrogram (high-frequency). The sleep state alsocorrelated with a consistent decrease in the M.I. values as observed inthe time-comodulogram. When the M.I. was small, the PAC preferred phasefluctuated dramatically.

FIG. 9 shows phase amplitude coupling (PAC) is altered in PD, and thatPAC measures can provide information about the sleep state (M1recordings shown).

In exemplary versions, algorithms and methods may be applied topreexisting implanted or prospective patients with DBS systems to treatPD, essential tremor, dystonia, epilepsy, pain, etc. The algorithms maybe uploaded into the firmware with systems capable of brain sensing anddelivery of stimulation. Therapy may be applied to de-novo patients withsleep disorders (i.e. insomnia, REM sleep disorder behavior, circadiandisorders, restless leg syndrome, etc.) alone or co-morbid with otherconditions.

The sensing algorithms may be used to detect and monitor traditionalsleep stage waves and correlate with clinical, physiological (i.e.polysomnography) or other objective measures of patient behavior to helpgive clinicians a diagnosis or monitor progress of conventionaltherapies. This may also combine with therapeutic algorithms andstimulations to perform closed loop/on demand stimulation tospecifically modulate brain circuits to improve brain waves inparticular circuits or to improve externally monitored sleep/quality oflife.

Stimulation parameters may match, appose, or otherwise be in somemathematical relationship with the brain wave frequencies detected ormay be patient specific (determined with trialing, for example).Stimulation parameters may also be varying and/or vary with the varioussleep stages and wave patterns throughout a sleep session. Stimulationmay simply be a constant (frequency, amplitude, pulse width, wave shape)chronic setting used only during specific sleep cycle time or throughoutthe day to maintain the optimal sleep circuit tone to promote healthysleep behavior. Algorithms may also learn and evolve based on patient,clinician, or sensor inputs.

In prospective or existing implants with such capabilities, methods mayinvolve a session of externalization of the DBS lead(s) to performsensing/recording and stimulation to calibrate/tailor the algorithm tothe patient's specific need; brain recordings or observed/recordedbehavior may be used in combination with other sensors. The DBS systemmay comprise the traditional embodiment where a deep brain implantedlead is connected to a subcutaneous extension (or directly connected),which connects to an implanted pulse generator someplace in the body.

Other embodiments may be leadless or extension-less, whereby aminimally-invasive miniature pulse generator with surface electrodes isimplanted and fixated (using, e.g., device body anchors) into a deepbrain or other brain target to perform sensing and stimulation thatimproves sleep symptoms. The device may have a primary or rechargeablebattery/power supply or be inductively powered by external energies(such as radio frequency (RF), WiFi, microwave, ultrasound, bioenergy,etc.), and may communicate wirelessly and with high fidelity withexternal control devices and sensors.

It is noted that the site for sensing and stimulation may be the same,or they may be different sites in the brain (or other CNS or PNStargets). For example, sleep stages may be detected at the corticallevel and modulation therapy may be delivered via thalamic or otherbasal ganglia targets. Similarly, sleep stages may be detected inthalamus/STN/GP and therapy may be delivered via modulation of thetrigeminal or vagal nerves.

A minimally invasive implant may be a traditional DBS implant procedureor may involve a full asleep/anesthetized procedure with direct MRI(traditional or high field) targeting prediction. The device may bedelivered through a minimal (3 mm) twist drill cranial hole orextra-cranially via vasculature or CSF channels using robotics ortraditional stereotactic methods.

Therapies provided may be multimodal, in which the original indicationis a sleep disorder, and a subsequent comorbid condition arises (e.g.,Parkinson's, etc.), or vice versa, and the system can adapt to treat thenew conditions (which need not be sleep-related) as well. Additionalembodiments of evaluating and modulating sleep using the DBS system 10are described below.

Sleep Evaluation System with DBS

In certain embodiments, device and software modules may be implementedusing the on-board processor of an IPG 12 associated with a DBS system10 to score sleep stages using an electrode of a DBS lead (e.g. such aselectrode selected from the first set of electrodes 32 or the second setof electrodes 36 on IPG 12, see FIG. 2) implanted adjacent to one ormore subcortical brain structures (see FIG. 1A). The system can classifysleep stages ‘on-line’ using the IPG on-board processor or ‘off-line’ bytransferring the sleep data from the IPG to a remote computer, forexample wirelessly. In the on-line/on-board mode, signal processingalgorithms are executed in the IPG 12 to identify and classify inreal-time the sleep stage and temporal features of sleep signals (e.g.K-complexes, spindles) as well as spectral features (e.g. power indifferent frequency bands). In the off-line mode, the DBS device storesin non-volatile memory (e.g. flash) the monopolar or differentialpotentials (or a down-sampled version of these potentials) from the DBSlead. The recordings may then be transferred to a host computer, a cloudservice, or other remote computing device for off-line staging of sleep.In various embodiments, the off-line mode may be used with IPG deviceshaving low-computational capabilities that are unable to perform sleepstaging or to minimize battery consumption associated with onboardprocessing.

The sleep status of the subject can be assessed using monopolar and/orbipolar recordings from one or more subcortical structures (e.g. thesubthalamic nucleus (STN), the globus pallidus internal (GPI) segment,the globus pallidus external (GPe) segment, or the thalamus) and/orother brain areas if applicable (e.g. as identified by cortical surfaceelectrocorticography (ECoG) arrays). In some embodiments, monopolarrecordings can be used to accurately sense low-frequency oscillations,whereas in other embodiments bipolar recordings can be used to improvethe sensing capabilities at higher frequencies.

Surprisingly, it has been determined that spindles and K complexes,observed via EEG and ECoG recordings and similar to those used fortraditional sleep staging studies, are also observed in the subcorticalstructures of NHPs. This surprising observation, which was made withNHPs, has in turn been used to develop a DBS system that scores thesleep stage of a subject using sleep scoring approaches applied topotentials measured in subcortical structures. Thus in variousembodiments, the sleep scoring mode of the DBS system 10 uses automaticdetection of one or more of: spindles, K-complexes, and relative power(i.e. power normalized with respect to total power across measuredfrequencies) in different frequency bands (e.g. delta, theta, alpha,beta) to classify and score the sleep stages. In general, the relativepower in frequency bands can be used to determine NREM and REM stagesbased on the observation that all sleep stages have high delta and thetapower, whereas NREM has low beta and gamma power and REM has high betaand gamma power. Spindles are generally detected based on the envelopeof oscillations in the 11-16 Hz band (although the specific frequencyband to use can be recording site-dependent, for example, STN spindledetection may in some cases require a lower frequency range, as shown inFIG. 10) and time domain feature extraction techniques based onwavelets. The K-complexes are generally detected using the envelope ofoscillations in the 0.5-2 Hz band and time domain feature extractiontechniques based on wavelets. Software routines for spindle andK-complex detection can be uploaded onto the device firmware to performdetection in the on-line/on-board mode.

In some embodiments, onboard sensors in the IPG such as tri-axialaccelerometers or tri-axial gyroscopes (if available) may be used toverify that the subjects are resting and thereby as a secondary metricto validate the sleep states.

In further embodiments, wearable sensors such as accelerometers andgyroscopes may be used to determine motion of the limbs during REM sleepand to provide information (e.g. for use by clinicians or closed-loopDBS algorithms) about REM sleep behavior disorder (RBD) signs (e.g.acting out vivid dreams). Sensors of heart rate and blood pressure mayalso be used together with the IPG data/algorithms to verify REM sleepstages and to supplement the sleep recordings from the subcortical DBSelectrodes. In certain embodiments these sensors may transfer data tothe IPG wirelessly.

In still other embodiments, the sleep evaluation algorithm may bepersonalized to a particular subject and may be optimized with feedbackfrom traditional polysomnography (PSG) recordings, recordings obtained,for example, in a sleep clinic setting or in a residence usinghome-based mobile devices to collect data and to evaluate the bestcontacts/contact pairs to use for sensing and classification. A softwareplatform (e.g. software used for IPG programming or another platform)can execute the optimization algorithms (e.g. gradient-basedoptimization, least squares) to optimize the selection of electrodesthat best predict the PSG assessments.

DBS System with Automatic Sleep Mode Control to Minimize StimulationDuring Sleep and Maximize Battery Life

In some embodiments, particular sleep stages including NREM and REM maybe detected and identified based on data indicative of cortical orsubcortical activity, for example monopolar and/or differential(bipolar) potentials recorded from DBS leads. Identification ofparticular sleep stages may then be used to modulate the operation of animplantable device such as the DBS system 10, as discussed furtherbelow.

Identification of particular sleep stages may be based at least in parton signal characteristics such as the amplitude of low frequencyoscillations in the STN and/or GPi, which are generally higher in allthe sleep stages than in the awake state. In various embodimentsdisclosed herein, the sleep mode algorithms of the DBS system 10 may usereal-time measurements of low-frequency power and/or proportions ofpower in different frequency bands (e.g. delta, theta, alpha, beta,gamma) to identify whether the subject is awake or asleep.

In further embodiments, algorithms such as those used by the IPG 12 toidentify spindles and K complexes in real-time may also be used in thesleep mode to verify whether the subject is in an NREM sleep stage (orother sleep stage, such as REM) and to minimize the occurrence of falsesleep detections. Accordingly, when a sleep state is detected the DBSsystem 10 (as guided by the sleep mode algorithm) may automaticallyreduce therapeutic stimulation (e.g. used for treating a condition otherthan a sleep disorder) by a desired level (e.g. 80%, a figure that maybe configurable by the subject or by a clinician/physician); thus, theDBS system 10 with ‘sleep mode’ may automatically detect sleep anddecrease therapeutic stimulation. When data indicates that the subjectis waking up, the sleep mode may be automatically suspended/disabled andthe DBS therapeutic settings may be restored to alleviate the symptomsof the subject (e.g. motor symptoms in PD). This automatically detectedand managed sleep mode provides more comfort to patients thanmanually-operated sleep modes in which the patients must manually turnthe device on or off (e.g. using an external programmer device) orprogram the times of activation and deactivation of the electricalstimulation ahead of time. For example, using the automatic sleep mode asubject who wakes up unexpectedly in the middle of the night (e.g. touse the restroom) may have his or her therapeutic settings restored(which in some embodiments may be done gradually so that the therapy isramped up) to relieve symptoms to enable the subject to get up and moveabout, without having to use the programmer to manually turn the deviceon.

Sleep Modulation

In various embodiments, monitoring and detection of sleep stages may beused to modulate the sleep of the subject in a ‘sleep enhancement mode.’The sleep enhancement mode may be activated by the subject or aclinician, which may be performed, for example, via thepatient/clinician IPG programmers.

A subject's sleep may be modulated/facilitated by actively amplifyinglow-frequency (e.g. slow-wave, delta, theta, alpha) neural activity andthereby promoting or enhancing natural rhythms that are associated withvarious sleep stages. By promoting natural rhythms for periods of timethat are comparable to those observed in healthy subjects, embodimentsof the DBS system 10 disclosed herein may regularize sleep in DBSsubjects who want to improve their sleep or in other subjects (who maynot be in need of DBS therapy) who have been diagnosed with a sleepdisorder.

Thus, a novel feature of embodiments of the DBS system 10 disclosedherein is the ability to amplify sleep-related rhythms in corticalregions of the brain of the subject based on cortical potentials evokedby electrical stimulation in subcortical structures. Amplification ofcortical and thalamic potentials at low-frequency via sub corticalstimulation promotes the synchronization of thalamocortical circuits andthe disconnection between peripheral stimuli and cortical regions,ultimately facilitating non-REM sleep.

In various embodiments, a particular electrode is selected to be astimulation electrode based on the particular electrode producing thelargest evoked potentials in cortical regions. The particular electrodethat is selected is used to amplify low-frequency oscillations (0.1-30Hz) in the cortex, as it is the most capable of modulating corticalactivity (i.e. has the highest ‘controllability’). Controllabilitymeasurements (which are used to select the sleep modulation electrode),such as the peak to peak amplitude, can be calculated online by the IPGor offline by a host computer, cloud service, or other remote computingdevice. In various embodiments, the cortical potentials may be measuredvia non-invasive EEG recordings or intra-cranial ECoG (if available).

In certain embodiments, sensors may also be placed in the peripheralnervous system (e.g. electromyography (EMG) electrodes, spinal cordleads, peripheral nerve sensors) to be used instead of, or in additionto, cortical output and to measure DBS cortical controllability forelectrode selection. In various embodiments, the electrode(s) forstimulation may be selected during an IPG programming session and maynot necessarily be those used for therapeutic purposes (e.g. toalleviate motor symptoms). Therefore, embodiments of sleep modulationand DBS therapy (e.g. motor relief in Parkinson's disease) may beperformed using the same or different electrical stimulation sources andDBS electrodes.

If the DBS electrode(s) used for treatment of primary symptom(s) (e.g.motor symptoms in PD) are different from those selected for sleepmodulation, and if the IPG has two independent current sources, the DBSsystem 10 in certain embodiments may simultaneously deliver DBS for bothsleep and the primary symptom. In other embodiments, if the sameelectrode(s) and/or current source(s) need to be used for both sleepmodulation and for treatment of the primary symptom(s), the system candeliver the sleep-specific stimulation pattern superimposed on theprimary DBS treatment stimulation pattern, which may be adjusted asnecessary to maintain balance of electrical charge delivered.

In some embodiments, neuromodulation of the target brain structures thatare part of the sleep circuitry may be performed using other techniquesinstead of (or in addition to) electrical stimulation via implantedelectrodes. For example, neuromodulation may be performed usingtechniques such as transcranial focused ultrasound, transcranialmagnetic stimulation (TMS), transcranial direct current stimulation(tDCS), and/or transcranial alternating current stimulation (TACS). Infurther embodiments, the DBS system 10 may be integrated with a drugdelivery system which may be used instead of (or in addition to)electrical or other stimulation described above. In this embodiment,drug therapy (e.g. levodopa) may be adjusted based on sensing by the DBSdevice 10; a drug may be delivered to the subject, for example, byinterfacing with a drug delivery system (e.g. patch/pump systems such asDuopa or NeuroDerm). In certain embodiments, various sleepdisorder-related medications may also be administered to the subjectbased on sleep sensing/evaluation. For example, medications may bedelivered to individuals with narcolepsy to wake them up during a sleepattack or to prevent an attack, where the medication may be administeredusing on-command signals sent from the DBS system 10 based on the sleepsensing/evaluation algorithms.

Open Loop Mode

In various embodiments, the control of cortical evoked responses may beachieved via open-loop and/or closed-loop modes. In the open-loop mode,the system does not take into account neural oscillatory activityalready present in the subcortical or cortical structures. Instead, thesystem delivers patterned stimulation (e.g. low-frequency bursts oramplitude-modulated stimulation) to promote amplification oflow-frequency oscillations in the thalamocortical networks that areassociated with sleep. This may continue throughout the sleeping period,as determined by the scheduler (defined below) or by the user/subject.In certain embodiments, stimulation may include a charge-balanced pulsetrain whose current amplitude and/or frequency is modulated such thatmaximal energy is delivered at a defined frequency Fc (e.g. square,triangular, sinusoidal envelopes), with the rationale that brain signalswill become entrained to the stimulation. Fc is the frequency at whichamplification of thalamocortical oscillations is desired. Exemplars ofstimulation signals may include: a Fc Hz pulse train; a pulse train withhigh carrier frequency (e.g. traditional 130 Hz) that is amplitudemodulated by a Fc Hz sinusoidal envelope; a pulse train with highcarrier frequency (e.g. traditional 130 Hz) that is frequency modulatedby a Fc Hz sinusoidal signal; or burst stimulation with N pulses andinter-burst frequency Fc.

Closed Loop Mode

In particular embodiments, at least two modes of closed-loop sleepneuromodulation are possible: (1) DBS feedback mode and (2) cortical orexternal sensor feedback mode (see schematic diagrams of one embodimentof the system shown in FIGS. 11 and 12). In the DBS feedback mode (e.g.as discussed above), feedback may be obtained from the same DBS leadthat has been implanted for therapeutic purposes (e.g. to treat sleepdisorders, Parkinson's disease, essential tremor, dystonia, etc.). Therationale behind using the DBS leads for sensing is that subcorticalstructures typically targeted for DBS therapies (e.g. STN, GP, thalamus)are functionally connected and synchronized with thalamic and corticalregions (e.g. motor cortex, supplementary motor area) and other brainareas involved with sleep (e.g. connections to brainstem nuclei involvedwith sleep such as the peduncolopontine nucleus). In the closed-loopwith cortical feedback mode, on the other hand, feedback is obtainedfrom recordings from ECoG electrodes implanted in the cortex. Inparticular embodiments of the closed-loop with cortical feedback mode,feedback may be obtained from EEG electrodes placed non-invasively onthe scalp or from any external sensor capable of sensing cortical output(e.g. spinal cord leads or EMG electrodes) instead of from ECoGelectrodes.

In either mode (i.e. DBS or cortical feedback), the feedback signals maybe filtered in the frequency band where amplification of oscillations isintended; various filtering approaches, for example a Butterworthfilter, may be used.

In various embodiments, electrical stimulation artifacts produced bycurrent conductance through brain tissue, but not by neuronal activity,may be removed from the feedback signal using a filter such as aninfinite impulse response (IIR) filter or a finite impulse response(FIR) filter. The filter parameters may be calculated during a DBSprogramming session, and these calculations may be performed usingsystem identification algorithms (e.g. output error, instrumentalvariable, subspace identification, ARX, ARMAX). A schematic of theartifact removal system is presented in FIG. 12. Time-varying, onlinesystem identification techniques (e.g. recursive least squaresestimator, recursive polynomial estimator, Kalman Filter, or Gradientoptimization) may also run on the IPG onboard computer to estimate thefilter parameters online. This online identification may be useful whenthe artifact shape and/or size vary over time.

FIG. 12 shows a schematic of an embodiment of an artifact removal systemshown as part of the system of FIG. 11. The filter parameters may beobtained via online (in the IPG) or offline (e.g. in a host computer oran IPG programmer) system identification of the artifacts (e.g. usingany system identification method). The artifacts and evoked potentialsmay be differentiated by using data in which both anodal and cathodalstimulation are delivered. The artifacts from cathodal and anodalstimulation cancel out, whereas the evoked responses do not.

In embodiments of the closed-loop mode, the stimulator may deliver atrain of N pulses with intra-burst frequency Fi that is phased-locked tothe measured oscillations in the target frequency band with centerfrequency Fc, where the number of pulses (N) and intra-burst frequency(Fi) may be configurable parameters. Additionally, the amplitude of thestimulation pulses can be configured to increase and decrease with arate of change equal to Rp. In other words, the stimulation pulseamplitude follows a trapezoidal profile with slopes equal to Rp. Asmooth transition between pulse amplitudes instead of an abrupttransition can help reduce side effects (e.g. paresthesias in PDpatients implanted with DBS in the STN).

The phase of the feedback signals (e.g. band-pass filtered in thefrequency band with center frequency Fc) used to deliver phasicstimulation may be calculated in real-time using a Hilbert Transformer(e.g. FIR filter) or other suitable phase estimation technique (e.g.phase-locked loop, PLL). The embedded IPG computer may use the phaseestimation to trigger the stimulation bursts with configured parametersN and Fi at the phase angle associated with maximum amplification of theoscillations in the feedback signal. The optimal phase angle may beautomatically determined by the onboard IPG processor or by the hostcomputer connected wirelessly with the IPG via a search over anglesbetween 0 and 360 degrees. Optimization algorithms such as the GradientDescent or Bisection Algorithm can also be used to efficiently find anoptimal phase angle at which stimulation maximizes the amplitude of thetarget oscillations.

The optimal phase angle at which stimulation minimizes the amplitude oftarget oscillations may also be determined and used to cause destructiveinterference and reduce oscillations in a particular frequency band.Therefore, in various embodiments the method described herein may beextended to suppress neural oscillations across selected frequency bandsassociated with neurological and psychiatric disorders (e.g. epilepsy,dystonia, Parkinson's disease, depression, obsessive compulsivedisorder).

Sleep Scheduler

In certain embodiments of the sleep modulation algorithm, a ‘sleepscheduler’ may be provided which permits a subject or clinician todesign a desired sleep pattern, or ‘sleep architecture’ (i.e. a healthyarchitecture target). The targeted frequency band with center frequencyFc may be controlled by an outer-loop scheduler that tracks the desiredsleep architecture (note that Fc is used in both the open-loop andclosed-loop modes described above and so the sleep scheduler isapplicable to either mode).

The target sleep architecture may be defined in the IPG programmingsession by selecting the desired time windows for each stage (N1, N2,N3, REM) in each cycle of sleep (e.g. two to six cycles total). The timewindows may be configured differently in each cycle; for example, longerperiods of REM sleep may be desired in the third and fourth cycles tomimic the sleep patterns of healthy subjects. Thus the target frequencyband and its center frequency Fc may vary slowly over time, starting inthe alpha band and moving to the theta and delta bands as timeprogresses. The target frequency may be rapidly increased following thedelta band to serve as a reference frequency for REM sleep. The rate ofchange to increase Fc may also be configured. When the target frequencyFc reaches the beta band, the scheduler may turn off the phasicstimulation aimed to entrain thalamocortical low-frequency oscillations.

In various embodiments, the sleep scheduler may also enable beta-band(˜10-30 Hz) phasic stimulation to promote synchrony in the beta band andthereby reduce body movements associated with REM sleep behaviordisorder (RBD). The rationale behind this idea is that beta bandstimulation has been reported to increase akinesia in Parkinson'sdisease patients. Phasic stimulation may be more effective in amplifyingbeta oscillations than open-loop stimulation with frequency in the betaband. Although akinesia may not be desired during waking hours, inpatients with RBD it may be desirable to attenuate excessive movementsthat can occur during REM sleep stage. After the REM target periodcompletes, the scheduler may decrease the target frequency towards thedelta band to cycle through N1, N2, and N3 stages again. The subject ortheir clinician may decide to program more than two incursions into theN3 stage. In sum, the target frequency of the sleep architecture followsand mimics the dominant oscillatory frequencies observed in healthysleep architectures and serves as a reference for the inner-loop phasicstimulation algorithm to amplify neural activity in specific frequencybands.

The outer-loop controller that schedules the target frequency band foramplification may operate in an open-loop mode or a closed-loop mode. Inthe open loop mode, the target frequency band and its center frequencymay be changed according to a predetermined program without taking intoaccount the actual sleep stage of the subject. The changes in the targetfrequency may occur in a stepwise (FIG. 13) or continuous manner. FIG.13 shows a schematic of a reference frequency generated by an outer-loopscheduler, where Fc is the center frequency for the frequency band inwhich phasic closed-loop stimulation may be delivered to amplifyoscillations. The reference profiles may be configured during theprogramming sessions.

In the closed-loop mode, the scheduler may wait until the actualdominant oscillatory power is at the target frequency band for thedesired duration before moving to the next target frequency band. In theclosed loop mode, the changes in target frequency band occur in steps(discrete transitions). Identification of time domain signal features(e.g. spindles, K-complexes) and spectral characteristics of the neuralsignals may help the closed-loop scheduler to confirm whether thesubject is in a REM or NREM sleep stage.

In some embodiments, sensors measuring EMG, heart rate, and/or bloodpressure can be used to improve the classification between REM andnon-REM stages, based on the observation that REM sleep is generallycharacterized by higher heart rate and blood pressure than non-REMstages and by low-amplitude EMG readings as compared to non-REM andawake stages. A smart-watch with EMG and blood pressure sensorsconnected to the IPG (e.g. wirelessly using Bluetooth, ZigBee, or othersuitable communication technology) may be used to improve theaforementioned sleep classification.

In various embodiments, sleep modulation may be provided for particularsleep disorders, including: insomnia/excessive daytime sleepiness; sleepattacks/narcolepsy; and REM sleep behavior disorder:

Insomnia/Excessive Daytime Sleepiness

It is anticipated that use of embodiments of the DBS system 10 toregularize various sleep stages may enhance sleep metrics such as sleepefficiency, REM start time, and number of sleep cycles (i.e. the numberof times REM stage sleep occurs), which may be favorable for subjectswith insomnia. It is anticipated that improved sleep efficiency willimprove (i.e. reduce) daytime sleepiness.

Sleep Attacks/Narcolepsy

Sleep attacks (i.e. a sudden involuntary episode of sleep) andnarcolepsy are often considered to occur as a side effect ofmedications. In various embodiments, sleep detection processing may beused during waking hours to alert the patient of sleep-like activity andto send a signal to an external device (e.g. an alarm on a patientprogrammer or a smart watch) to the subject to wake up and takeappropriate medication (if applicable), or to trigger a different set ofstimulation parameters that may interfere with the subject's transitioninto sleep.

REM Sleep Behavior Disorder

As discussed above, the sleep scheduler may also enable beta-band(˜10-30 Hz) phasic stimulation aimed at promoting synchrony in the betaband and thereby reduce body movements associated with REM sleepbehavior disorder (RBD).

EXAMPLE

Data collected in a non-human primate (NHP) during phasic stimulation(as described above) shows that it is possible to modulate low-frequencyneural activity in the STN using STN recordings. The upper panel of FIG.14 is a spectrogram which shows the power of STN local field potentials(LFPs, differential potentials) during phasic stimulation of the STN ofa non-human primate, where the STN LFPs were used for feedback.Stimulation was delivered at different phase angles of the STNoscillations, as shown in the lower panel of FIG. 14. The targetfrequency was Fc=12 Hz, the bandwidth BW=4 Hz, the intra-burst frequencyFi=165 Hz, and the number of pulses for the burst N=4. The oscillatorypower at 12 Hz was amplified when the stimulation was delivered atspecific phase angles (−90 to 0 degrees) of the neural oscillations. Thepower was suppressed at approx. 90 degrees. Stimulation evokedpotentials phase-locked to the neural oscillations are the primary causeof changes in power observed in the spectrogram. These data show thatphasic stimulation is capable of modulating neural activity insubcortical structures at low frequency. LFPs were recorded andstimulation was delivered using a DBS lead (NuMed) which is ascaled-down version of human DBS leads. The algorithms in thisexperiment were executed in a real-time control computer with a samplingfrequency of 25 KHz. LFPs were recorded and transmitted to the real-timecontrol computer using a TDT Neurophysiological recording system (25 KHzsampling rate). The TDT system was also used to deliver electricalstimulation to the STN (current stimulation).

The present invention has been described in terms of one or morepreferred embodiments, and it should be appreciated that manyequivalents, alternatives, variations, and modifications, aside fromthose expressly stated, are possible and within the scope of theinvention.

1-55. (canceled)
 56. A method for controlling a deep brain stimulator(DBS) implanted in a brain of a subject, comprising the steps of:receiving electrical data obtained from a subcortical structure of thebrain of the subject; determining a sleep stage of the subject based onanalyzing the electrical data; and adjusting control of the DBS based ondetermining the sleep stage of the subject.
 57. The method of claim 56,wherein the subcortical structure is one of: the subthalamic nucleus(STN), the globus pallidus internal (GPI) segment, the globus pallidusexternal (GPe) segment, or the thalamus.
 58. The method of claim 56,wherein the electrical data comprises a plurality of frequency bands,and wherein determining the sleep stage of the subject based on theelectrical data further comprises: determining that the subject is in aREM sleep stage or an NREM sleep stage based on at least one ofcalculating a relative power associated with each of the plurality offrequency bands or identifying at least one of a K complex or a spindlein the electrical data.
 59. The method of claim 58, wherein adjustingcontrol of the DBS further comprises: reducing therapeutic stimulationof the DBS based on determining that the subject is in the REM sleepstage or the NREM sleep stage.
 60. The method of claim 59, furthercomprising: receiving further electrical data obtained from thesubcortical structure of the brain of the subject, determining that thesubject is no longer in the REM sleep stage or the NREM sleep stagebased on analyzing the further electrical data, and resuming thetherapeutic stimulation of the DBS based on determining that the subjectis no longer in the REM sleep stage or the NREM sleep stage.
 61. Themethod of claim 56, wherein determining the sleep state of the subjectbased on analyzing the electrical data further comprises: determiningthat the sleep state of the subject is asleep during a preprogrammedwaking period, and wherein adjusting control of the DBS based ondetermining the sleep state of the subject further comprises:transmitting a signal from the DBS to alert the subject to wake up. 62.A method for modulating sleep in a subject, comprising: stimulating asubcortical structure of a brain of the subject using a deep brainstimulator (DBS) implanted in the brain of the subject to alter a sleepstage of the subject, stimulating comprising applying a patternedstimulation to the subcortical structure using the DBS.
 63. The methodof claim 62, wherein the patterned stimulation comprises: a chargebalanced pulse train comprising a maximal energy delivered at a centerfrequency Fc to amplify oscillations having the center frequency Fc. 64.The method of claim 63, further comprising: receiving feedback signalsobtained from the brain of the subject, identifying a phase angle of thefeedback signals associated with a maximum amplification of oscillationswith center frequency Fc, and stimulating the subcortical structure ofthe brain of the subject at the identified phase angle using at leastone electrode of the DBS.
 65. The method of claim 64, wherein receivingthe feedback signals obtained from the brain of the subject furthercomprises: removing electrical stimulation artifacts from the feedbacksignals.
 66. The method of claim 64, wherein receiving the feedbacksignals obtained from the brain of the subject further comprises:receiving feedback signals from at least one of: the DBS, a scalpelectrode, an implanted electrocorticography (ECoG) array, a spinal cordlead, or an electromyography (EMG) electrode.
 67. The method of claim63, further comprising: changing the center frequency Fc using an outerloop scheduler of the DBS to generate an updated patterned stimulation,the updated patterned stimulation designed to stimulate the subcorticalstructure of the brain of the subject to enter at least one of a REMsleep stage or an NREM sleep stage, and wherein stimulating thesubcortical structure of the brain of the subject further comprises:stimulating the subcortical structure of the brain of the subject byapplying the updated patterned stimulation to the subcortical structure.68. The method of claim 67, further comprising: receiving feedbacksignals obtained from the brain of the subject, analyzing the feedbacksignals to determine that the brain of the subject has been in at leastone of the REM sleep stage or the NREM sleep stages for a target periodof time, and wherein changing the center frequency Fc further comprises:changing the center frequency Fc based on analyzing the feedback signalsto determine that the brain of the subject has been in at least one ofthe REM sleep stage or the NREM sleep stage for the target period oftime.
 69. A deep brain stimulation (DBS) system, comprising: acontroller including a processor and instructions that, when executed bythe processor, configure the DBS system to: receive electrical dataobtained from a subcortical structure of a brain of a subject; determinethat the subject is in a REM sleep stage or an NREM sleep stage based onanalyzing the electrical data; and adjust therapeutic stimulation of theDBS based on determining that the subject is in the REM sleep stage orthe NREM sleep stage.
 70. The system of claim 69, wherein the controlleris further configured to: receive further electrical data obtained fromthe subcortical structure of the brain of the subject, determine thatthe subject is no longer in the REM sleep stage or the NREM sleep stagebased on analyzing the further electrical data, and resume thetherapeutic stimulation of the DBS based on determining that the subjectis no longer in the REM sleep stage or the NREM sleep stage.
 71. A deepbrain stimulation (DBS) system, comprising: a controller including aprocessor and instructions that, when executed by the processor,configure the DBS system to: stimulate a subcortical structure of abrain of a subject to alter a sleep stage of the subject, stimulatingcomprising applying a patterned stimulation to the subcorticalstructure.
 72. The system of claim 71, wherein the patterned stimulationcomprises: a charge balanced pulse train comprising a maximal energydelivered at a center frequency Fc to amplify oscillations with centerfrequency Fc.
 73. The system of claim 72, wherein the controller isfurther configured to: receive feedback signals obtained from the brainof the subject, identify a phase angle of the feedback signalsassociated with a maximum amplification of oscillations with centerfrequency Fc, and stimulate the subcortical structure of the brain ofthe subject at the identified phase angle using at least one electrodeof the DBS.
 74. The system of claim 72, wherein the controller isfurther configured to: change the center frequency Fc using an outerloop scheduler of the DBS to generate an updated patterned stimulation,the updated patterned stimulation designed to stimulate the subcorticalstructure of the brain of the subject to enter at least one of a REMsleep stage or an NREM sleep stage, and wherein the controller, whenstimulating the subcortical structure of the brain of the subject, isfurther configured to: stimulate the subcortical structure of the brainof the subject by applying the updated patterned stimulation to thesubcortical structure.
 75. The system of claim 74, wherein thecontroller is further configured to: receive feedback signals obtainedfrom the brain of the subject, analyze the feedback signals to determinethat the brain of the subject has been in at least one of the REM sleepstage or the NREM sleep stage for a target period of time, and whereinthe controller, when changing the center frequency Fc, is furtherconfigured to: change the center frequency Fc based on analyzing thefeedback signals to determine that the brain of the subject has been inat least one of the REM sleep stage or the NREM sleep stage for a targetperiod of time.